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All Journal JOURNAL OF APPLIED INFORMATICS AND COMPUTING JOURNAL OF INFORMATICS AND COMPUTER SCIENCE
Aidilof, Hafizh Al-Kautsar
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Computer Science & IT

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ANALISIS SENTIMEN PADA TWITTER TERHADAP APLIKASI MOBILE JKN MENGGUNAKAN METODE NAÏVE BAYES CLASSIFIER Yunizar, Zara; Rusnani, Rusnani; Ardian, Zalfie; Aidilof, Hafizh Al-Kautsar; Maulana, O.K.Muhammad Majid
JOURNAL OF INFORMATICS AND COMPUTER SCIENCE Vol 9, No 2 (2023): Oktober 2023
Publisher : Ubudiyah Indonesia University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33143/jics.v9i2.3261

Abstract

Abstrak- Mobile JKN merupakan aplikasi yang dibuat oleh BPJS Kesehatan untuk memudahkan beberapa masalah administrasi peserta, sehingga peserta tidak perlu datang ke Kantor Cabang karena dapat dilakukan atau diselesaikan dengan aplikasi ini. Tetapi aplikasi ini tidak jarang memiliki beberapa kendala, sehingga menimbulkan penilaian yang kurang baik terhadap pelayanan tersebut. Media sosial Twitter cocok digunakan untuk tempat mengungkapkan perasaan seseorang, membagikan dan mendapatkan informasi terkini, serta komentar atau opini tentang segala hal yang banyak dikenal dan penggunanya pun cukup banyak. Salah satu cara untuk mencari komentar atau opini dari penulis tentang suatu hal, entitas atau subjek tertentu sehingga dapat diklasifikasikan menjadi opini positif, negatif ataupun netral dapat digunakan dengan Analisis Sentimen. Analisis sentimen dapat dilakukan dengan menggunakan Algoritma Naïve Bayes. Naïve Bayes yang nantinya akan mengelompokkan berdasarkan peluang atau probabilitas, dimana dihitung sekumpulan probabilitas dengan menjumlahkan frekuensi dan kombinasi nilai dari dataset yang diberikan. Pada proses implementasinya, dataset yang didapatkan berjumlah 1001 tweet, dengan perbandingan data training dan data testing yaitu 80:20. Penelitian ini menghasilkan 488 tweet positif, netral 193, dan negatif 320. Sedangkan untuk menghitung akurasinya menggunakan confussion matrix dengan akurasi yaitu 69.65%, presisi 62.18%, recall 60.44%.Kata Kunci: Mobile JKN, Twitter, Analisis Sentimen, Naïve Bayes Classifier.Abstract- Mobile JKN is an application created by BPJS Health to facilitate several administrative problems for participants, so that participants do not need to come to the Branch Office because they can be done or resolved with this application. However, this application often has several problems, giving rise to an unfavorable assessment of the service. Twitter social media is suitable for use as a place to express one's feelings, share and get the latest information, as well as comments or opinions about everything that is widely known and has quite a lot of users. One way to look for comments or opinions from writers about a particular thing, entity or subject so that they can be classified into positive, negative or neutral opinions can be used with Sentiment Analysis. Sentiment analysis can be done using the Naïve Bayes Algorithm. Naïve Bayes will then group based on chance or probability, where a set of probabilities is calculated by adding up the frequencies and combinations of values from the given dataset. In the implementation process, the dataset obtained was 1001 tweets, with a ratio of training data and testing data of 80:20. This research produced 488 positive tweets, 193 neutral and 320 negative. Meanwhile, to calculate the accuracy, a confusion matrix was used with an accuracy of 69.65%, precision 62.18%, recall 60.44%.Keywords: Mobile JKN, Twitter, Sentiment Analysis, Naïve Bayes Classifier.
Comparative Analysis of the C5.0 Algorithm and Other Machine Learning Models for Early Detection of Multi-Class Heart Disease Mardhatillah, Mardhatillah; Aidilof, Hafizh Al-Kautsar; Aidilof, Asrianda
Journal of Applied Informatics and Computing Vol. 9 No. 4 (2025): August 2025
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v9i4.9753

Abstract

Cardiovascular diseases represent the leading cause of mortality worldwide, making accurate and early detection a critical factor for effective medical intervention and improved patient prognosis. While machine learning (ML) offers promising tools for predictive diagnostics, many existing studies rely on single-algorithm approaches or less-than-robust validation methods, thereby limiting the generalizability and real-world applicability of their findings.This study aims to conduct a rigorous, head-to-head comparative evaluation of multiple machine learning algorithms for the multi-class classification of heart disease, with the goal of identifying the most effective and reliable model for this complex clinical task.We utilized a private dataset comprising 300 patient medical records, each described by 11 clinically relevant features. To ensure a robust and unbiased evaluation, a stratified 5-fold cross-validation methodology was employed. Five widely-used classification algorithms were evaluated: Naïve Bayes (NB), Logistic Regression (LR), Random Forest (RF), a C5.0-analog Decision Tree (DT), and Support Vector Machine (SVM). Model performance was assessed using standard metrics, including accuracy, precision, recall, and F1-score.The comparative analysis revealed that the Naïve Bayes algorithm delivered superior performance, achieving the highest mean accuracy of 43.33% (±4.22%). It also led in other key metrics with a mean precision of 43.40%, recall of 43.64%, and an F1-score of 41.26%. Other algorithms, such as Logistic Regression (40.67% accuracy) and Random Forest (39.33% accuracy), demonstrated competitive performance but were ultimately surpassed by the Naïve Bayes model in this specific multi-class classification context.This research underscores the critical importance of employing robust validation techniques and comprehensive comparative analyses to identify optimal models for clinical applications. The Naïve Bayes algorithm emerges as a strong candidate for developing a reliable clinical decision support system for the early differentiation of various heart conditions, providing a foundation for future data-driven diagnostic tools.
Co-Authors Aidilof, Asrianda Mardhatillah, Mardhatillah Maulana, O.K.Muhammad Majid Rusnani Rusnani Zara Yunizar